The Society of Toxicology (SOT) presented Dr. Richard Beger, Division of Systems Biology, with the 2016 Translational Impact Award on March 13, 2016. The award citation outlined Dr. Beger's leadership "using computational modeling, metabolomics, and proteomics to develop translational approaches to improve drug safety, disease detection/management, and human health."
Dr. Beger is the co-inventor of a unique Quantitative Spectrometric Data Activity Relationship (QSDAR), a modeling technique that has been used to predict efficacy and toxicity. A recent model used by FDA's Center for Drug Evaluation and Research (CDER) predicts the adverse outcome of drug-induced phospholipidosis. Dr. Beger’s and his colleagues’ work with metabolomics studies has characterized biomarkers in preclinical studies that have carried into the clinic, improved drug safety and individualized patient care. Results of kidney toxicity studies in animals and in the clinic can be used to evaluate kidney damage in children undergoing cardiopulmonary bypass surgery. Similarly, biofluid-accessible biomarkers of hepatotoxicity are used to assess damage in children undergoing therapeutic treatment or with overdose of acetaminophen.
Dr. Beger's award address was titled, "Translational Non-Invasive Biomarkers of Acetaminophen-Induced Liver injury.”
For additional information, the SOT Annual Meeting program is available online (see pages 70 and 294); or contact William Slikker, Jr., Ph.D., Director, FDA/NCTR or Richard Beger, Ph.D., Director, Biomarers and Alternative Models Branch, Division of Systems Biology, FDA/NCTR.
DILIrank: Large Dataset With Improved Annotation for DILI Risk in Humans
NCTR scientists published a new dataset (DILIrank) which now contains 1,036 FDA-approved drugs that have been ranked according to their potential to cause drug-induced liver injury (DILI), making it the largest publically available annotated DILI dataset. DILI risk is predicted using a newly refined annotation schema that adds evidence of causality to the previously used drug-labeling information in the Liver Toxicity Knowledge Base Benchmark Dataset. Of the 1,036 drugs listed, 192 were defined and verified as vMost-DILI concern, 278 as vLess-DILI concern, 312 as vNo-DILI concern, and 254 as Ambiguous-DILI concern (previously predicted as Most- or Less-DILI concern, but lacked causality under the new schema). This dataset may be a useful resource for building DILI predictive models that use high-throughput or in silico methodologies.
For additional information, see online at Drug Discovery Today or contact Weida Tong, Ph.D., Director, Division of Bioinformatics and Biostatistics, FDA/NCTR.
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